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AI for Computational Audition: Sound and Music Processing

Artificial intelligence (AI) technology has promoted the transformation of traditional audio technology to intelligence. Using these emerging technologies to innovate and develop audio production and processing methods has become a key research direction in the field of computational audition. In recent years, AI technologies, such as deep learning, is becoming increasingly ubiquitous in the music industry, empowering music experimentation and has led to major progress in the innovation and development of audio production and reproduction technologies. 

Nowadays, the application of AI algorithms and techniques is ubiquitous and transversal. Fields that take advantage of AI advances include Sound and Music Processing. The advances in interdisciplinary research potentially yield new insights that may further advance the AI methods in this field. This special issue aims to spur new research lines in AI-driven sound and music processing, especially within interdisciplinary research scenarios. 

This special issue aims to collect research on AI for computational audition with a focus on music. The principal goal is to bring together scholars interested in the research on the theory and technology to realize the integration of traditional method and emerging technologies, the application and comparative analysis of different intelligent technologies in music creation.

Lead Guest Editor
Zijin Li, Central Conservatory of Music, China
Email: lzijin@ccom.edu.cn

Guest Editors
Wenwu Wang, University of Surrey, UK 
Kejun Zhang, Zhejiang University, China 
Mengyao Zhu, Shanghai University, China

  1. Accurately representing the sound field with high spatial resolution is crucial for immersive and interactive sound field reproduction technology. In recent studies, there has been a notable emphasis on effici...

    Authors: Zining Liang, Wen Zhang and Thushara D. Abhayapala
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2024 2024:13
    Content type: Empirical Research Published on:

  2. Melody harmonization, which involves generating a chord progression that complements a user-provided melody, continues to pose a significant challenge. A chord progression must not only be in harmony with the ...

    Authors: Shangda Wu, Yue Yang, Zhaowen Wang, Xiaobing Li and Maosong Sun
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2024 2024:4
    Content type: Empirical Research Published on:

  3. The task of bandwidth extension addresses the generation of missing high frequencies of audio signals based on knowledge of the low-frequency part of the sound. This task applies to various problems, such as a...

    Authors: Pierre-Amaury Grumiaux and Mathieu Lagrange
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:51
    Content type: Empirical Research Published on:

  4. Snoring affects 57 % of men, 40 % of women, and 27 % of children in the USA. Besides, snoring is highly correlated with obstructive sleep apnoea (OSA), which is characterised by loud and frequent snoring. OSA ...

    Authors: Jingtan Li, Mengkai Sun, Zhonghao Zhao, Xingcan Li, Gaigai Li, Chen Wu, Kun Qian, Bin Hu, Yoshiharu Yamamoto and Björn W. Schuller
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:43
    Content type: Empirical Research Published on:

  5. Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two...

    Authors: Jian Guan, Youde Liu, Qiuqiang Kong, Feiyang Xiao, Qiaoxi Zhu, Jiantong Tian and Wenwu Wang
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:42
    Content type: Methodology Published on:

  6. In recent years, the speaker-independent, single-channel speech separation problem has made significant progress with the development of deep neural networks (DNNs). However, separating the speech of each inte...

    Authors: Chunxi Wang, Maoshen Jia and Xinfeng Zhang
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:41
    Content type: Methodology Published on:

  7. Traffic congestion can lead to negative driving emotions, significantly increasing the likelihood of traffic accidents. Reducing negative driving emotions as a means to mitigate speeding, reckless overtaking, ...

    Authors: Lekai Zhang, Yingfan Wang, Kailun He, Hailong Zhang, Baixi Xing, Xiaofeng Liu and Fo Hu
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:34
    Content type: Empirical Research Published on: